We present a novel boundary-aware face alignment algorithm by utilising
boundary lines as the geometric structure of a human face to help facial
landmark localisation. Unlike the conventional heatmap based method and
regression based method, our approach derives face landmarks from boundary
lines which remove the ambiguities in the landmark definition. Three questions
are explored and answered by this work: 1. Why using boundary? 2. How to use
boundary? 3. What is the relationship between boundary estimation and landmarks
localisation? Our boundary- aware face alignment algorithm achieves 3.49% mean
error on 300-W Fullset, which outperforms state-of-the-art methods by a large
margin. Our method can also easily integrate information from other datasets.
By utilising boundary information of 300-W dataset, our method achieves 3.92%
mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on
AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training
and testing across different factors, including poses, expressions,
illuminations, makeups, occlusions, and blurriness. Dataset and model will be
publicly available at this https URL

本文提出了一种新颖的边界感知的面部对齐算法，利用边界线作为人脸的几何结构来帮助面部标记的本地化，并从边界线中推导出人脸的标记，使标记的定义不再含糊。通过使用面部的边界信息，该方法在 300-W Fullset 上取得了 3.49％±0.07％的平均误差，比现有的最先进方法表现出色，并且可以轻松地整合来自其他数据集的信息。